Offline Handwritten Text Recognition (HTR) is the task of digitalizing text that is depicted in an image and is a well-established task in the field of Computer Vision, while is widely considered as a challenging task due to the vast variety of writing styles (see Figure REF ).
HTR is, by its nature, a sequence transduction task. The goal is to convert a sequence of features, extracted from an image, to a sequence of text. Primarily, this was implemented with Hidden Markov Models [1]}, [2]}, [3]}, while contemporary deep-learning based HTR systems are relying on a class of Recurrent Neural Networks (RNNs), the Long Short Term Memory (LSTMs)  [4]}  [5]}, [6]}, [7]}.
A well-known paradox, know as Sarye's Paradox supports that a cursive written word cannot be recognized unless has been segmented and cannot be segmented unless is recognized.
This paradox has been addressed by the Connectionist Temporal Classification (CTC) Algorithm [8]}, a Dynamic Programming algorithm, that does not take into account the exact segmentation of the characters, rather their possible alignments.It cares about the monotonical ordering of the targets that are depicted on the image.
CTC algorithm has been extensively used on the HTR research and can be utilized for training Deep Learning models since it can be formulated as a differentiable loss function for end-to-end training.
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